Peters, Stuart; (2024) Model-based inference of species population history from orphan ancient and modern mtDNA data. Doctoral thesis (Ph.D), UCL (University College London).

Text Stuart_Peters_Stuart Peters PhD Thesis_Final.pdf - Accepted Version Access restricted to UCL open access staff until 1 October 2024. Download (7MB)

Abstract

In recent years nuclear genomic data has come to dominate population genetic studies on the evolutionary history of various species, leaving a large amount of ‘orphan’ mitochondrial DNA (mtDNA) unpublished or unanalysed. However, mtDNA data can be informative on species’ histories when coupled with model-based inference, which usually involves some assessment of how probable observed genetic data are under a range of hypothesised demographic histories and associated parameter values. This thesis describes the analysis of a mix of modern and ancient ‘orphan’ mtDNA data sets from various species, to infer aspects of their population history. Using an inferential framework based on coalescent simulation and Approximate Bayesian Computation (ABC) I examine: (1) biogeographic structuring of Solenodon paradoxus in response to geotectonic history; (2) possible population continuity of the Orkney vole with a now extirpated Great Britain population; (3) the recolonisation of North America by Amara alpina beetles after the last glacial maximum; and (4) the effective population size density and cytochrome b mutation rate of Hypselognathus rostratus, in the Seychelles. The latter involved the development of novel computer code to infer cycles of disconnection and reconnection among islands using bathymetric and elevation data, and a sea-level reconstruction covering the past 5.3 million years, to shape coalescent simulations. My analyses illustrate that useful inferences can be made using ‘orphan’ mtDNA data when coupled with explicit modelling and ABC. The development of a method for analysing archipelago biogeographic history could be applied more generally to other species. Overall, this work has the potential to inform on how various species might respond to ongoing environmental change and to understand biogeographic restructuring.

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